Design method of wiring harness for electric control box

Abstract

The present invention provides a method for designing a harness for an electric control box. The method constructs a three-dimensional model of the interior of the electric control box and forms a wiring space. A fitness function is formed according to the path length of the harness, obstacles in the path of the harness, the number of corners in the path of the harness, the degree of electromagnetic interference in the path of the harness, the space utilization rate of the path of the harness, and a wall constraint. A reference wiring path is combined to generate multiple initial harness paths in the wiring space to initialize a dung beetle population. An improved adaptive step-size improved dung beetle algorithm is used to iteratively generate a global optimal solution, thereby forming an optimal harness path. The method can comprehensively consider the requirements of multiple objectives of harness design, and under the premise of considering the wall constraint, the overall process optimal layout of multi-branch cables in a complex environment is achieved, and a wiring result with both excellent conductor paths and branch structures is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of power electronics, and in particular to a design method for a wiring harness of an electric control box. Background Art

[0002] In the field of power electronics, wiring harness design is an important part of electric control box design, and its design quality is of great significance to the reliability and maintainability of the electric control system. The wiring harness design methods in the prior art are mainly divided into two categories: rule-based design methods and software-assisted design methods.

[0003] The rule-based design method mainly relies on the experience and judgment of engineers, and its design process is cumbersome and prone to errors. The rule-based design method usually involves multiple steps such as wire material selection, wire diameter determination, wire harness layout and fixation, and requires engineers to have a deep understanding of the physical and electrical properties of the wire harness. In addition, this method also requires the design of each wire harness one by one, which cannot meet the needs of batch design and rapid response.

[0004] The software-assisted design method uses professional harness design software, such as EPLAN, to design. This method can improve design efficiency and accuracy and reduce the error rate of manual operation. By importing the schematic diagram and harness data of the electric control box, the software can automatically generate a harness design plan, including wire material selection, wire diameter determination, harness layout and fixation, etc.

[0005] However, the internal structure of the electric control box is relatively complex, and the wiring harness path inside the electric control box has many objectives and constraints. The existing wiring harness design methods have problems such as inaccurate design data, unreasonable wiring, and low change efficiency. How to provide an electric control box wiring harness design method that can integrate the requirements of multiple objectives of wiring harness design and find the global optimal solution or approximate optimal solution of the wiring harness path in the electrical system. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method for designing a wiring harness for an electric control box, which can integrate the requirements of multiple objectives of wiring harness design, and under the premise of considering wall constraints, realize the overall optimal layout of multi-branch cables in a complex environment, and obtain wiring results with excellent wire paths and branch structures.

[0007] In order to solve the above problems, the present invention provides a method for designing a wiring harness for an electric control box, comprising: generating a reference wiring path; preprocessing the wiring space, constructing a three-dimensional model of the interior of the electric control box and forming a wiring space, dividing the wiring space into grid units, each grid unit representing an area in the wiring space; forming a fitness function according to the path length of the wiring harness, obstacles in the path of the wiring harness, the number of corners in the path of the wiring harness, the degree of electromagnetic interference in the path of the wiring harness, the spatial utilization rate of the path of the wiring harness, and the wall constraint; improving the dung beetle optimization algorithm to form an adaptive step-size improved dung beetle algorithm, the adaptive step-size improved dung beetle algorithm including a position update process; generating multiple initial positions in the wiring space in combination with the reference wiring path. The invention discloses a method for calculating the fitness value of each dung beetle in the dung beetle population according to the initial beam path; improving the dung beetle algorithm based on the adaptive step size, calculating the fitness value of each dung beetle individual in the dung beetle population according to the fitness function, and updating the position of each dung beetle individual; finding the dung beetle individual with the smallest fitness value, taking the fitness value of the dung beetle individual as the fitness value of the current iteration and taking the position of the dung beetle individual as the global optimal solution of the current iteration; if the number of iterations reaches the iteration number threshold, or the fitness value of the current iteration is less than the fitness threshold, or the difference between the fitness values ​​of two iterations is less than or equal to the preset value for multiple consecutive times, the algorithm is terminated, and the global optimal solution in the current iteration is output, and the global optimal solution is transformed into the optimal beam path.

[0008] In some embodiments, in the step of dividing the wiring space into grid units, the grid is encrypted in areas with dense wiring bundles, complex obstacles or curved paths, and other areas are divided into evenly distributed grids, and the grid units are tetrahedral grids or hexahedral grids.

[0009] In some embodiments, the fitness function is: ,in: , , , is a weight variable, with a value between 0 and 1. is the total length of the harness path, , For the The path length of the wire bundle, is the total number of harnesses, is the penalty for crossing obstacles in the total path of the harness, , For the The penalty for the path of the wire bundle crossing an obstacle, The value is 0 or 1. If the path of the wire bundle passes through an obstacle, The value of is 1, if If the path of the wire bundle does not pass through obstacles, then The value of is 0. is the total number of corners in the path of the harness, , For the The number of corners in the path of the wire bundle, is the total electromagnetic interference level in the wiring harness path, ， For the The current of the wire harness, For the The current of the wire harness, For the The path of the wire harness and the The distance between the paths of the wire bundles, is the total space utilization of the harness path, , is the volume of the available space inside the electric control box, For the The volume occupied by the path of the wire bundle, For wall constraints, , It is The distance between the path of the wire harness and the fixed wall inside the electric control box, is the penalty coefficient.

[0010] In some embodiments, the fixed wall surface is an inner wall of the electric control box or a surface of a sheet metal part fixed inside the electric control box.

[0011] In some embodiments, in the position update process of the adaptive step-size improved dung beetle algorithm, the adaptive step-size update formula is: , the position update formula is: ,in, is the initial step length, For dung beetle individuals The step length, is the step size adjustment factor, , For dung beetle individuals At the location of the wiring space, , Represents a dung beetle individual In the wiring space The position of the dimensional space, is the dimension of the wiring space, For dung beetle individuals At the new location in the wiring space, For the target direction, , The target direction is the first The direction of the dimensional space, is the position of the dung beetle individual located at the global optimal solution position in the wiring space, For dung beetle individuals The fitness difference between the dung beetle individual at the global optimal solution position, , For dung beetle individuals The Euclidean distance from the dung beetle individual at the global optimal solution position, , Represents a dung beetle individual In the wiring space The position of the dimensional space, Indicates that the dung beetle individual located at the global optimal solution position is in the first The position of the dimensional space, is a very small positive number.

[0012] In some embodiments, the adaptive step-size improved dung beetle algorithm further includes a breeding operation process, in which a dung beetle individual having a fitness value lower than a target fitness value is randomly selected. and dung beetle individuals and reproduce to produce breeding balls , the dung beetle individual and the dung beetle individual The positions in the wiring space are respectively and , the breeding balls generated The location of the wiring space is ,in: , , , is the dimension of the wiring space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the breeding ball In the wiring space The position of the dimensional space, , is a random number, For the breeding ball In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space dimensional space position.

[0013] In some embodiments, the adaptive step-size improved dung beetle algorithm further includes a thief interference process, for each dung beetle individual , during the thief interference process, a disturbance vector is randomly generated , In the wiring space dimensional space, then the dung beetle individual Updated position after being disturbed by thieves , .

[0014] In some embodiments, the step of generating multiple initial wiring beam paths in the wiring space in combination with the reference wiring path further includes: randomly generating multiple initial wiring beam paths in the wiring space; adding the reference wiring path to the initial wiring beam path, or adjusting the node position of a portion of the path in the initial wiring beam path according to the reference wiring path.

[0015] In some embodiments, the improved dung beetle algorithm based on the adaptive step size calculates the fitness value of each dung beetle individual in the dung beetle population according to the fitness function, and in the step of updating the position of each dung beetle individual, it is determined whether the new position of each dung beetle individual is located within the wiring space and does not collide with obstacles in the wiring space. If the new position of a dung beetle individual exceeds the range of the wiring space or collides with an obstacle in the wiring space, the new position is corrected or a new position is generated.

[0016] In some embodiments, the iteration number threshold is 100, the fitness threshold is 10 -10 .

[0017] The above technical scheme constructs a three-dimensional model of the interior of the electric control box and forms a wiring space. A fitness function is formed according to the path length of the wire harness, obstacles in the path of the wire harness, the number of corners in the path of the wire harness, the degree of electromagnetic interference in the path of the wire harness, the spatial utilization of the path of the wire harness, and the wall constraint. A plurality of initial wire harness paths are generated in the wiring space in combination with a reference wiring path to initialize the dung beetle population. The improved adaptive step size is used to improve the dung beetle algorithm to iteratively generate a global optimal solution, thereby forming an optimal wire harness path. The multi-objective requirements of the wire harness design can be comprehensively considered. Under the premise of considering the wall constraint, the overall process optimal layout of multi-branch cables in a complex environment can be achieved, and a wiring result with both excellent wire path and branch structure can be obtained.

[0018] It should be understood that the above general description and the detailed description below are exemplary and explanatory only and cannot limit the present invention. The techniques, methods and devices known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered as part of the authorization specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 The present invention is a flowchart of a method for designing a wiring harness for an electric control box provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Figure 1It is a flow chart of the electric control box harness design method provided by an embodiment of the present invention. The electric control box harness design method comprises: step S11, generating a reference wiring path; step S12, wiring space preprocessing, constructing a three-dimensional model of the interior of the electric control box and forming a wiring space, dividing the wiring space into grid units, and each grid unit represents an area in the wiring space; step S13, forming a fitness function according to the path length of the harness, obstacles in the path of the harness, the number of corners in the path of the harness, the degree of electromagnetic interference in the path of the harness, the spatial utilization of the path of the harness, and the wall constraint; step S14, improving the dung beetle optimization algorithm to form an adaptive step-size improved dung beetle algorithm, and the adaptive step-size improved dung beetle algorithm includes a position update process; step S15, generating multiple initial harnesses in the wiring space in combination with the reference wiring path. Path, initialize the dung beetle population according to the initial beam path; step S16, based on the adaptive step size to improve the dung beetle algorithm, calculate the fitness value of each dung beetle individual in the dung beetle population according to the fitness function, and update the position of each dung beetle individual; step S17, find the dung beetle individual with the smallest fitness value, use the fitness value of the dung beetle individual as the fitness value of the current iteration and use the position of the dung beetle individual as the global optimal solution of the current iteration; step S18, if the number of iterations reaches the iteration number threshold, or the fitness value of the current iteration is less than the fitness threshold, or the difference between the fitness values ​​obtained by two iterations for multiple consecutive times is less than or equal to the preset value, then the algorithm terminates, outputs the global optimal solution in the current iteration, and transforms the global optimal solution into the optimal beam path.

[0023] Referring to step S11, a reference wiring path is generated. In this embodiment, the wiring data can be input into the auxiliary design software and the reference wiring path can be generated. Wherein, the auxiliary design software is EPLAN, and the wiring data includes the type, terminal, and color of the wires used. EPLAN can automatically generate a wiring harness design scheme based on the schematic diagram of the electric control box and the wiring data, including wire material selection, wire diameter determination, wiring harness layout and fixation. In this embodiment, the wiring harness layout is used as the reference wiring path.

[0024] Referring to step S12, the wiring space is preprocessed to construct a three-dimensional model of the interior of the electric control box and form a wiring space, and the wiring space is divided into grid units, each grid unit representing an area in the wiring space.

[0025] In this step, CAD tools or 3D scanning technology can be used to build a three-dimensional model of the interior of the electric control box. Specifically, add necessary details to each electrical device model inside the electric control box, such as mounting holes, interfaces, labels, etc. These details are very important for accurate wiring harness layout and subsequent analysis; ensure that the size and proportion of each electrical device model are accurate, and adjust them by referring to the technical specifications of the equipment or actual measurement data. For standard electrical components, existing models can be found or imported from the library of related software. If there is no ready-made model, a new model can be created using modeling tools based on the size and shape of the equipment.

[0026] In this step, a suitable coordinate system and reference plane need to be determined to accurately locate the electrical equipment. The created electrical equipment model is placed in the three-dimensional space according to the actual installation position, and the position and direction of the equipment can be adjusted using the software's move, rotate, align and other tools to ensure that the relative position relationship between them meets the requirements of the electrical schematic diagram, so as to form a wiring space containing various electrical equipment models inside the electrical control box.

[0027] In this step, when the wiring space is divided into grid units, the grid is encrypted in the area with dense wire bundles, complex obstacles or curved paths to improve local accuracy; uniformly distributed grid division is used for other areas to ensure the effectiveness and efficiency of calculation. The grid unit is a tetrahedral grid or a hexahedral grid. The tetrahedral grid has a high success rate and is suitable for complex geometric shapes. The number of grids after hexahedral grid division is small and the calculation accuracy is high. This embodiment uses a tetrahedral grid.

[0028] Referring to step S13, a fitness function is formed based on the path length of the wire harness, obstacles in the path of the wire harness, the number of corners in the path of the wire harness, the degree of electromagnetic interference in the path of the wire harness, the space utilization of the path of the wire harness, and the wall constraint.

[0029] The optimization goals of designing the wiring harness path in this step include minimizing the length of the wiring harness path, avoiding obstacles, reducing bends and corners, optimizing electromagnetic compatibility, and maximizing space utilization. In order to prevent the wiring harness inside the electric control box from being suspended and to be more regular, a wall constraint is added in this step to constrain the distance between the wiring harness and the fixed wall inside the electric control box. The wall constraint requires that the wiring harness path be routed as close as possible to the fixed wall or equipment housing inside the electric control box. Accordingly, the wall constraint can not only save space, but also reduce the risk of the wiring harness being exposed to the outside, thereby improving the compactness and safety of the electrical system. The wall constraint minimizes the distance between the path and the wall, thereby achieving wall-mounted routing of the path.

[0030] In this step, the fixed wall surface is the inner wall of the electric control box or the surface of the sheet metal part fixed inside the electric control box, so that the wire harness inside the electric control box is not suspended and can be more regular.

[0031] The fitness function is: ,in: , , , is a weight variable, with a value between 0 and 1. is the total length of the harness path, , For the The path length of the wire bundle, is the total number of harnesses, is the penalty for crossing obstacles in the total path of the harness, , For the The penalty for the path of the wire bundle crossing an obstacle, The value is 0 or 1. If the path of the wire bundle passes through an obstacle, The value of is 1, if If the path of the wire bundle does not pass through obstacles, then The value of is 0. is the total number of corners in the path of the harness, , For the The number of corners in the path of the wire bundle, is the total electromagnetic interference level in the wiring harness path, ， For the The current of the wire harness, For the The current of the wire harness, For the The path of the wire harness and the The distance between the paths of the wire bundles, is the total space utilization of the harness path, , is the volume of the available space inside the electric control box, For the The volume occupied by the path of the wire bundle, For wall constraints, , It is The distance between the path of the wire harness and the fixed wall inside the electric control box, is the penalty coefficient.

[0032] Weight variables , , , The specific value depends on the following factors: application scenario, wiring cost, security and reliability.

[0033] For application scenarios, if the space is very limited, maximizing space utilization may be more important. The value of is relatively large, such as between 0.2 and 0.4; if it is in an environment with strong electromagnetic interference, the weight of electromagnetic compatibility optimization is higher. The value of is relatively large, for example, between 0.2 and 0.3.

[0034] In terms of wiring cost, minimizing the path length of the harness is usually directly related to cost. If cost is the main consideration, The weight of The possible value is between 0.2 and 0.3.

[0035] In terms of safety and reliability, avoiding obstacles and reducing bending corners are crucial to the safety and reliability of the wiring harness. If the safety and reliability requirements are relatively high, you can improve , For example, , The values ​​are between 0.1 and 0.2.

[0036] The selection of weights is a relatively subjective process and needs to be adjusted and optimized according to specific design requirements, environmental conditions and engineering experience. In practical applications, the most appropriate combination of weight values ​​can be gradually determined through multiple tests and simulations, and the weights of the fitness function can be finally determined.

[0037] Referring to step S14, the dung beetle optimization algorithm is improved to form an adaptive step-size improved dung beetle algorithm, and the adaptive step-size improved dung beetle algorithm includes a position update process.

[0038] The Dung Beetle Optimizer (DBO) algorithm was proposed by simulating the social behavior of dung beetles pushing dung balls to store food. It divides the behavior of dung beetles into four categories: ball-pushing dung beetles, breeding ball dung beetles, small dung beetles, and thief dung beetles, and abstractly models the social behavior of each category of individuals. In order to gain an advantage in the competition with other dung beetles, dung beetles push dung balls as efficiently as possible, so they often choose a relatively optimal position in the process of pushing the ball. In terms of path selection, the behavior of dung beetles pushing balls is affected by natural environmental factors, such as light source exposure and wind direction. When there is sufficient light, the path of dung beetles pushing balls is usually a straight line; when encountering obstacles, dung beetles will choose the direction of pushing the ball by rotating. Dung beetles usually choose to lay eggs and breed offspring in dung balls. To ensure the safety of their offspring, dung beetles will push breeding balls to a safe area. After the eggs in the breeding balls hatch into small dung beetles, they will choose the safest foraging area to forage. There are also thief dung beetles in the dung beetle group, which will steal the dung balls of other dung beetles.

[0039] The present invention provides an adaptive step-size improved dung beetle algorithm, which is innovatively extended on the basis of the existing dung beetle optimization algorithm.

[0040] In the position update process of the adaptive step-size improved dung beetle algorithm, the adaptive step-size update formula is: , the position update formula is: ,in, is the initial step length, For dung beetle individuals The step length, is the step size adjustment factor, , For dung beetle individuals At the location of the wiring space, , Represents a dung beetle individual In the wiring space The position of the dimensional space, is the dimension of the wiring space, For dung beetle individuals At the new location in the wiring space, For the target direction, , The target direction is the first The direction of the dimensional space, is the position of the dung beetle individual located at the global optimal solution position in the wiring space, For dung beetle individuals The fitness difference between the dung beetle individual at the global optimal solution position, , For dung beetle individuals The Euclidean distance from the dung beetle individual at the global optimal solution position, , Represents a dung beetle individual In the wiring space The position of the dimensional space, Indicates that the dung beetle individual located at the global optimal solution position is in the first The position of the dimensional space, is a very small positive number.

[0041] Referring to step S15, a plurality of initial wiring harness paths are generated in the wiring space in combination with the reference wiring path, and the dung beetle population is initialized according to the initial wiring harness paths.

[0042] Initializing the dung beetle population provides a starting state for the adaptive step-size improved dung beetle algorithm. In the wire beam path planning, a series of coordinate points can be generated in a three-dimensional space to represent the starting position, middle position and end position of the wire beam path represented by the dung beetle individuals. With the iteration of the algorithm, these paths are gradually optimized.

[0043] This step further includes: (1) randomly generating a plurality of initial wiring harness paths in the wiring space; (2) adding the reference wiring path to the initial wiring harness path, or adjusting the node positions of a portion of the path in the initial wiring harness path according to the reference wiring path.

[0044] Regarding step (1), N dung beetle individuals are randomly generated. The location of the wiring space is , , Represents a dung beetle individual In the wiring space The position of the dimensional space, The method of randomly generating the positions of dung beetle individuals can be widely distributed in the entire wiring space, which helps the adaptive step-size improved dung beetle algorithm to start exploring in different areas and increase the possibility of finding a global optimal solution.

[0045] Regarding step (2), in this embodiment, the reference wiring path is added to the initial wiring path, and accordingly, the reference wiring path is converted into one or more dung beetle individuals in the adaptive step-size improved dung beetle algorithm and added to the dung beetle population; in another embodiment, with the reference wiring path as a reference, part of the path in the initial wiring path randomly generated in step (1) is adjusted so that the randomly generated initial wiring path is close to the reference wiring path to a certain extent, and accordingly, the adjusted wiring path is converted into dung beetle individuals in the adaptive step-size improved dung beetle algorithm, and the dung beetle population is updated. Using the reference wiring path, a relatively good starting point can be provided for the adaptive step-size improved dung beetle algorithm, thereby accelerating the convergence speed of the algorithm.

[0046] In this embodiment, the dung beetle population size is 30.

[0047] Referring to step S16, based on the adaptive step-size improved dung beetle algorithm, the fitness value of each dung beetle individual in the dung beetle population is calculated according to the fitness function, and the position of each dung beetle individual is updated. The adaptive step-size improved dung beetle algorithm continuously updates the position of the dung beetle individual through iteration, thereby optimizing the path of the dung beetle individual.

[0048] The fitness value of each dung beetle individual is calculated according to the fitness function in step S13. In the present invention, the smaller the fitness value, the better the path.

[0049] In the rolling behavior, the position update formula in step S14 is used to update the position of the dung beetle individual. After the position is updated, it is determined whether the new position of each dung beetle individual is located in the wiring space and does not collide with the obstacles in the wiring space. If the new position of a dung beetle individual exceeds the range of the wiring space or collides with the obstacles in the wiring space, the new position is corrected or a new position is generated. Thus, the position of the dung beetle individual is adjusted in time.

[0050] In this embodiment, the adaptive step-size improved dung beetle algorithm also includes a breeding operation process, in which a dung beetle individual having a fitness value lower than a target fitness value is randomly selected. and dung beetle individuals and reproduce to produce breeding balls , the dung beetle individual and the dung beetle individual The positions in the wiring space are respectively and , the breeding balls generated The location of the wiring space is ,in: , , , is the dimension of the wiring space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the breeding ball In the wiring space The position of the dimensional space, , is a random number, For the breeding ball In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space dimensional space position.

[0051] The target fitness value is a preset fitness value. Based on the target fitness value, the dung beetle individuals whose fitness values ​​are less than the target fitness value are classified into a good population, and the individuals in the good population perform the breeding operation process according to a breeding probability.

[0052] The breeding operation process algorithm can increase the diversity of the path. In the process of optimizing the wire harness path, new individuals are generated through the breeding balls, thereby introducing new path exploration points at different stages. For example, when the adaptive step-size improved dung beetle algorithm falls into a local optimum for a period of time, some new path starting points or intermediate points can be randomly generated by simulating the behavior of the breeding balls to expand the search space and increase the possibility of finding a better path. These newly generated points can be combined with existing path points to form new path candidate solutions, thereby avoiding the algorithm from converging to a local optimal solution too early.

[0053] In this embodiment, the adaptive step-size improved dung beetle algorithm also includes a thief interference process. , during the thief interference process, a disturbance vector is randomly generated , In the wiring space dimensional space, then the dung beetle individual Updated position after being disturbed by thieves , ,in, For dung beetle individuals At the location of the wiring space, is the dimension of the wiring space.

[0054] Individuals in the dung beetle population perform the thief interference process according to an interference probability.

[0055] The thief interference process can introduce certain uncertainties and competition mechanisms. In optimizing the wiring harness path, the thief can be regarded as a disturbance factor or a competition object. For example, when the adaptive step-size improved dung beetle algorithm is in the search process, the thief can randomly destroy or change the existing path, forcing the algorithm to re-evaluate and adjust the path to improve the robustness of the path. At the same time, the thief interference behavior can also prompt the algorithm to more actively find a better path to avoid being "stolen". This competition mechanism can increase the vitality of the algorithm and prevent the algorithm from falling into a stagnant state.

[0056] Referring to step S17, find the dung beetle individual with the smallest fitness value, use the fitness value of the dung beetle individual as the fitness value of the current iteration, and use the position of the dung beetle individual as the global optimal solution of the current iteration.

[0057] In one iteration, after obtaining the fitness values ​​and positions of all dung beetle individuals from step S16, find the dung beetle individual with the smallest fitness value, use the fitness value of the dung beetle individual as the fitness value of the current iteration, and use the position of the dung beetle individual as the global optimal solution of the current iteration. The global optimal solution of the current iteration is the global optimal position that the entire population can find in the current iteration. In each iteration, the dung beetle individual with the smallest fitness value is selected to continuously approach the global optimal solution.

[0058] In the present invention, the smaller the fitness value, the better the path, and the dung beetle individuals with lower fitness values ​​can be selected to enter the next iterative population.

[0059] Referring to step S18, if the number of iterations reaches the iteration number threshold, or the fitness value of the current iteration is less than the fitness threshold, or the difference between the fitness values ​​of two iterations is less than or equal to the preset value for multiple consecutive times, the algorithm terminates and outputs the global optimal solution in the current iteration. The global optimal solution is the global optimal position found by the entire population, and the global optimal solution is used to transform the optimal wire harness path. The optimal wire harness path is the final wire harness optimization solution.

[0060] In this embodiment, the iteration number threshold is 100, and the fitness threshold is 10 -10 .

[0061] The preset value is used to determine the difference between the fitness values ​​of two consecutive iterations. The smaller the difference between the fitness values ​​of two consecutive iterations, the smaller the difference, indicating that the algorithm iteration has approached the global optimal solution. In this embodiment, it can be set to a value proportional to the fitness value, for example, the preset value is 0.1% of the fitness value.

[0062] The above technical scheme constructs a three-dimensional model of the interior of the electric control box and forms a wiring space. A fitness function is formed according to the path length of the wire harness, obstacles in the path of the wire harness, the number of corners in the path of the wire harness, the degree of electromagnetic interference in the path of the wire harness, the spatial utilization of the path of the wire harness, and the wall constraint. A plurality of initial wire harness paths are generated in the wiring space in combination with a reference wiring path to initialize the dung beetle population. The improved adaptive step size is used to improve the dung beetle algorithm to iteratively generate a global optimal solution, thereby forming an optimal wire harness path. The multi-objective requirements of wire harness design can be comprehensively considered. Under the premise of considering the wall constraint, the overall process optimal layout of multi-branch cables in a complex environment can be achieved, and a wiring result with both excellent wire path and branch structure can be obtained.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion. The various embodiments in this specification are described in a related manner, and the same and similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0064] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. It should be noted that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for designing a wiring harness for an electric control box, characterized in that: include: generating a reference wiring path; Wiring space preprocessing, constructing a three-dimensional model inside the electric control box and forming a wiring space, dividing the wiring space into grid units, each grid unit represents an area in the wiring space; forming a fitness function according to the path length of the wire harness, obstacles in the path of the wire harness, the number of corners in the path of the wire harness, the degree of electromagnetic interference in the path of the wire harness, the space utilization rate of the path of the wire harness, and the wall constraint; improving the dung beetle optimization algorithm to form an adaptive step-size improved dung beetle algorithm, the adaptive step-size improved dung beetle algorithm includes a position update process, wherein the adaptive step-size update formula is: , the position update formula is: , is the initial step length, For dung beetle individuals The step length, is the step size adjustment factor, , For dung beetle individuals At the location of the wiring space, , Represents a dung beetle individual In the wiring space The position of the dimensional space, is the dimension of the wiring space, For dung beetle individuals At the new location in the wiring space, For the target direction, , The target direction is the first The direction of the dimensional space, is the position of the dung beetle individual located at the global optimal solution position in the wiring space, For dung beetle individuals The fitness difference between the dung beetle individual at the global optimal solution position, , For dung beetle individuals The Euclidean distance from the dung beetle individual at the global optimal solution position, , Represents a dung beetle individual In the wiring space The position of the dimensional space, Indicates that the dung beetle individual located at the global optimal solution position is in the first The position of the dimensional space, is a very small positive number; combining the reference wiring path to generate multiple initial wiring harness paths in the wiring space, and initializing the dung beetle population according to the initial wiring harness paths; improving the dung beetle algorithm based on the adaptive step size, calculating the fitness value of each dung beetle individual in the dung beetle population according to the fitness function, and updating the position of each dung beetle individual; finding the dung beetle individual with the smallest fitness value, taking the fitness value of the dung beetle individual as the fitness value of the current iteration and taking the position of the dung beetle individual as the global optimal solution of the current iteration; if the number of iterations reaches the iteration number threshold, or the fitness value of the current iteration is less than the fitness threshold, or the difference between the fitness values ​​of two iterations appears to be less than or equal to the preset value for multiple consecutive times, the algorithm terminates, outputs the global optimal solution in the current iteration, and transforms the global optimal solution into the optimal wiring harness path.

2. The method according to claim 1, characterized in that In the step of dividing the wiring space into grid units, the grid is encrypted in areas with dense wiring bundles, complex obstacles or curved paths, and other areas are divided into evenly distributed grids. The grid units are tetrahedral grids or hexahedral grids.

3. The method according to claim 1, characterized in that The fitness function is: ,in: , , , is a weight variable, with a value between 0 and 1. is the total length of the harness path, , For the The path length of the wire bundle, is the total number of harnesses, is the penalty for crossing obstacles in the total path of the harness, , For the The penalty for the path of the wire bundle crossing an obstacle, The value is 0 or 1. If the path of the wire bundle passes through an obstacle, The value of is 1, if If the path of the wire bundle does not pass through obstacles, then The value of is 0. is the total number of corners in the path of the harness, , For the The number of corners in the path of the wire bundle, is the total electromagnetic interference level in the wiring harness path, ， For the The current of the wire harness, For the The current of the wire harness, For the The path of the wire harness and the The distance between the paths of the wire bundles, is the total space utilization of the harness path, , is the volume of the available space inside the electric control box, For the The volume occupied by the path of the wire bundle, For wall constraints, , It is The distance between the path of the wire harness and the fixed wall inside the electric control box, is the penalty coefficient.

4. The method according to claim 3, characterized in that The fixed wall surface is the inner wall of the electric control box or the surface of a sheet metal part fixed inside the electric control box.

5. The method according to claim 1, characterized in that The adaptive step-size improved dung beetle algorithm also includes a breeding operation process, in which dung beetle individuals with fitness values ​​lower than the target fitness value are randomly selected. and dung beetle individuals Produce breeding balls , the dung beetle individual and the dung beetle individual The positions in the wiring space are respectively and , the breeding balls generated The position of the wiring space is ,in: , , , is the dimension of the wiring space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the breeding ball In the wiring space The position of the dimensional space, , is a random number, For the breeding ball In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space The position of the dimensional space, For the dung beetle individual In the wiring space dimensional space position.

6. The method according to claim 1, characterized in that The adaptive step-size improved dung beetle algorithm also includes a thief interference process, for each dung beetle individual , during the thief interference process, a disturbance vector is randomly generated , In the wiring space dimensional space, then the dung beetle individual Updated position after being disturbed by thieves , .

7. The method according to claim 1, characterized in that The step of generating multiple initial wiring harness paths in the wiring space in combination with the reference wiring path further includes: randomly generating multiple initial wiring harness paths in the wiring space; adding the reference wiring path to the initial wiring harness path, or adjusting the node positions of some paths in the initial wiring harness path according to the reference wiring path.

8. The method according to claim 1, characterized in that In the step of improving the dung beetle algorithm based on the adaptive step size, calculating the fitness value of each dung beetle individual in the dung beetle population according to the fitness function, and updating the position of each dung beetle individual, it is determined whether the new position of each dung beetle individual is located within the wiring space and does not collide with the obstacles in the wiring space. If the new position of a dung beetle individual exceeds the range of the wiring space or collides with the obstacles in the wiring space, the new position is corrected or a new position is generated.

9. The method according to claim 1, characterized in that: The iteration number threshold is 100, and the fitness threshold is 10 -10 .

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